System And Method For Automated Diagnosis Of Myocardial Ischemia Through Remote Monitoring

ABSTRACT

A system and method for automated diagnosis of myocardial ischemia through remote monitoring is described. Physiological measures comprising data either recorded on a regular basis by a medical device or derived therefrom is stored. Qualitative measures associated with the physiological measures are matched. Indications of myocardial ischemia are remotely identified. The qualitative measures for both of a reduction in exercise capacity and respiratory distress occurring contemporaneously are examined. The qualitative measures for angina that accompanies the reduction in exercise capacity and the respiratory distress are evaluated. A time course for each of the indications is determined. A patient status is formed comprising an onset of myocardial ischemia conditioned on the time course comprising a short duration.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 10/876,126, filed Jun. 24, 2004, pending; which is a divisionof U.S. Pat. No. 6,827,690, issued Dec. 7, 2004; which is a continuationof U.S. Pat. No. 6,638,284, issued Apr. 9, 2002, the disclosures ofwhich are incorporated by reference, and the priority filing dates ofwhich are claimed.

FIELD

The present invention relates in general to myocardial ischemia (ormyocardial infarction) diagnosis and analysis, and, in particular, to asystem and method for providing automated diagnosis of myocardialischemia through remote monitoring.

BACKGROUND

Presently, myocardial ischemia, usually from the narrowing of thecoronary arteries as a result of atherosclerosis, is one of the leadingcauses of cardiovascular disease-related deaths in the world.Clinically, myocardial ischemia involves a decreased oxygen and nutrientdelivery to the myocardium resulting from diminished coronary arteryblood flow which in turn leads primarily to abnormalities of leftventricular function and cardiac rhythm and the consequences thereof.Myocardial ischemia occurs when the demands of the heart for oxygen andnutrients are not met commensurately by available blood supply. Thephysiological effects of myocardial ischemia range from minimal to acomplete failure of cardiac pumping function depending upon the degreeof myocardial involvement and/or associated cardiac rhythmabnormalities. Clinical manifestations of myocardial ischemia includechest pain or discomfort (angina); respiratory distress, includingshortness of breath; fatigue; reduced exercise capacity or tolerance;and nausea.

Several factors make the early diagnosis and prevention of myocardialischemia, as well as the monitoring of the progression of myocardialischemia, relatively difficult. First, the onset of myocardial ischemiais generally subtle and sometimes occurs without any clinicalmanifestations perceptible to the patient. Often, the symptoms are mildand ignored. The patient may also compensate by changing his or herdaily activities in an unconscious manner to minimize symptoms. As aresult, myocardial ischemia can remain undiagnosed until more seriousproblems arise, such as severe congestive heart failure leading tocardiac arrest or pulmonary edema. Moreover, the susceptibility tosuffer from myocardial ischemia depends upon the patient's age, sex,physical condition, and other factors, such as diabetes, blood pressure,cholesterol and homocystine levels. No one factor is dispositive.Finally, annual or even monthly checkups provide, at best, a “snapshot”of patient wellness and the incremental and subtle clinicophysiologicalchanges which portend the onset or progression of myocardial ischemiaoften go unnoticed, even with regular health care. Documentation ofsubtle changes following initiation of therapy, that can guide andrefine further evaluation and therapy, can be equally elusive.

Nevertheless, taking advantage of frequently and regularly measuredphysiological measures, such as recorded manually by a patient, via anexternal monitoring or therapeutic device, or via implantable devicetechnologies, can provide a degree of detection and preventionheretofore unknown. For instance, patients already suffering from someform of treatable hart disease often receive an implantable pulsegenerator (IPG), cardiovascular monitor, therapeutic device, or similarexternal wearable device, with which rhythm and structural problems ofthe heart can be monitored and treated. These types of devices areuseful for detecting physiological changes in patient conditions throughthe retrieval and analysis of telemetered signals stored in an on-board,volatile memory. Typically, these devices can store more than thirtyminutes of per heartbeat data recorded on a per heartbeat, binnedaverage basis, or on a derived basis from, for example, atrial orventricular electrical activity, ST and T wave electrocardiographicchanges, coronary sinus blood flow and composition, cardiac enzymerelease, minute ventilation, patient activity score, cardiac outputscore, mixed venous oxygen score, cardiovascular pressure measures, andthe like. However, the proper analysis of retrieved telemetered signalsrequires detailed medical subspecialty knowledge, particularly bycardiologists.

Alternatively, these telemetered signals can be remotely collected andanalyzed using an automated patient care system. One such system isdescribed in a related, commonly owned U.S. Pat. No. 6,312,378, issuedNov. 6, 2001, the disclosure of which is incorporated herein byreference. A medical device adapted to be implanted in an individualpatient records telemetered signals that are then retrieved on aregular, periodic basis using an interrogator or similar interfacingdevice. The telemetered signals are downloaded via an internetwork ontoa network server on an regular, e.g., daily, basis and stored as sets ofcollected measures in a database along with other patient care records.The information is then analyzed in an automated fashion and feedback,which includes a patient status indicator, is provided to the patient.

While such an automated system can serve as a valuable tool in providingremote patient care, an approach to systematically correlating andanalyzing the raw collected telemetered signals, as well as annuallycollected physiological measures, through applied cardiovascular medicalknowledge to accurately diagnose the onset of a particular medicalcondition, such as myocardial ischemia, is needed. One automated patientcare system directed to a patient-specific monitoring function isdescribed in U.S. Pat. No. 5,113,869 ('869) to Nappholz et al. The '869patent discloses an implantable, programmable electrocardiography (ECG)patient monitoring device that senses and analyzes ECG signals to detectECG and physiological signal characteristics predictive of malignantcardiac arrhythmias. The monitoring device can communicate a warningsignal to an external device when arrhythmias are predicted. Like theECG morphology of malignant cardiac tachycardias, theelectrocardiographic diagnosis of myocardial ischemia is wellestablished and can be readily predicted using on-board signal detectiontechniques. However, the Nappholz device is limited to detectingtachycardias. The Nappholz device is patient specific and is unable toautomatically take into consideration a broader patient or peer grouphistory for reference to detect and consider the progression orimprovement of myocardial ischemia. In addition, the Nappholz devicedies not take into account other physiological or chemical measuresindicative of myocardial ischemia. Moreover, the Nappholz device has alimited capability to automatically Self-reference multiple data pointsin time and cannot detect disease regression even in the individualpatient. Also, the Nappholz device must be implanted and cannot functionas an external monitor. Finally, the Nappholz device is incapable oftracking the cardiovascular and cardiopulmonary consequences of anyrhythm disorder.

Consequently, there is a need for a systematic approach to detectingtrends in regularly collected physiological and chemical data indicativeof the onset, progression, regression, or status quo of myocardialischemia diagnosed and monitored using an automated, remote patient caresystem. The physiological data could be telemetered signals datarecorded either by an external or an implantable medical device or,alternatively, individual measures collected through manual means.Preferably, such an approach would be capable of diagnosing bothmyocardial ischemia conditions, as well as the symptoms of otherdiseases. In addition, findings from individual, peer group, and generalpopulation patient care records could be integrated into continuous,on-going monitoring and analysis.

SUMMARY

The present invention provides a system and method for diagnosing andmonitoring the onset, progression, regression, and status quo ofmyocardial ischemia using an automated collection and analysis patientcare system. Measures of patient cardiovascular information are eitherrecorded by an external or implantable medical device, such as an IPG,cardiovascular or heart failure monitor, or other therapeutic device, ormanually through conventional patient-operable means. The measures arecollected on a regular, periodic basis for storage in a database alongwith other patient care records. Derived measures are developed from thestored measures. Select stored and derived measures are analyzed andchanges in patient condition are logged. The logged changes are comparedto quantified indicator thresholds to detect the principalcardiovascular pathophysiological manifestations of myocardial ischemia:ST segment and/or T wave changes on the ECG, left ventricular wallmotion changes, increased coronary sinus lactate production, increasedserum creatinine kinase, increased serum troponin, increased ventriculararrhythmias, increased left ventricular end diastolic pressure, andreduced cardiac output.

An embodiment of the present invention provides a system and method forautomated diagnosis of myocardial ischemia through remote monitoring.Physiological measures comprising data either recorded on a regularbasis by a medical device or derived therefrom is stored. Qualitativemeasures associated with the physiological measures are matched.Indications of myocardial ischemia are remotely identified. Thequalitative measures for both of a reduction in exercise capacity andrespiratory distress occurring contemporaneously are examined. Thequalitative measure for angina that accompanies the reduction inexercise capacity and the respiratory distress are evaluated. A timecourse for each of the indications is determined. A patient status isformed comprising an onset of myocardial ischemia conditioned on thetime course comprising a short duration.

A further embodiment provides a system and method for automateddiagnosis of myocardial ischemia through remote monitoring.Physiological measures comprising data either recorded on a regularbasis by a medial device or derived therefrom are stored. Qualitativemeasures associated with the physiological measures are matched.Indications of myocardial ischemia are remotely identified. Thequalitative measures for angina are examined. The qualitative measuresfor one of a reduction in exercise capacity and of respiratory distress,which accompanies the angina are further evaluated. A time course foreach of the indications is determined. A patient status is formedcomprising an onset of myocardial ischemia conditioned on the timecourse comprising a short duration.

A further embodiment provides a system and method for providingautomated diagnosis of cardiac ischemia through ST segment monitoring.Physiological measures comprising data either recorded on a regularbasis by an implantable medical device or derived therefrom areassembled. Indications of cardiac ischemia are determined. Thephysiological measures that comprise electrocardial signals areidentified and a period of increased activity indicated thereby isdetected. A deviation in electrical potential during an ST segment ofthe electrocardial signal with in the period of increased activity isdetermined. A patient status comprising a form of cardiac ischemia basedon the deviation is formed.

A further embodiment provides a system and method for automatedtreatment of myocardial ischemia through ST segment monitoring.Physiological measures comprising data either recorded on a regularbasis by an implantable medial device or derived therefrom is assembled.Indications of myocardial ischemia are determined. The physiologicalmeasures that comprise electrocardial signals are identified and aperiod of increased activity indicated thereby is detected. An elevationin electrical potential during an ST segment of the electrocardialsignal within the period of increased activity is determined. Anintracardiac electrical therapy to treat the indications of myocardialischemia in response to the elevation in electrical potential isdelivered.

The present invention provides a capability to detect and track subtletrends and incremental changes in recorded patient information fordiagnosing and monitoring myocardial ischemia. When coupled with anenrollment in a remote patient monitoring service having the capabilityto remotely and continuously collect and analyze external or implantablemedical device measures, myocardial ischemia detection, prevention, andtracking regression from therapeutic maneuvers become feasible.

still other embodiments of the present invention will become readilyapparent to those skilled int eh art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an automated collection and analysispatient care system for diagnosing and monitoring myocardial ischemiaand outcomes thereof in accordance with the present invention;

FIG. 2 is a database schema showing, by way of example, the organizationof a device and derived measures set record for care of patients withmyocardial ischemia stored as part of a patient care record in thedatabase of the system of FIG. 1;

FIG. 3 is a database schema showing, by way of example, the organizationof a quality of life and symptom measures set record for care ofpatients with myocardial ischemia stored as part of a patient carerecord in the database of the system of FIG. 1;

FIG. 4 is a database schema showing, by way of example, the organizationof a combined measures set record for care of patients with myocardialischemia stored as part of a patient care record in the database of thesystem of FIG. 1;

FIG. 5 is a block diagram showing the software modules of the serversystem of the system of FIG. 1;

FIG. 6 is a record view shoeing, by way of example, a set of partialpatient care records for care of patients with myocardial ischemiastored in the database of the system of FIG. 1;

FIG. 7 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patent care records of FIG. 6;

FIGS. 8A-8B are flow diagrams showing a method for diagnosing andmonitoring myocardial ischemia and outcomes thereof using an automatedcollection and analysis patient care system in accordance with thepresent invention;

FIG. 9 is a flow diagram showing the routine for retrieving referencebaseline sets for use in the method of FIGS. 8A-8B;

FIG. 10 is a flow diagram showing the routine for retrieving monitoringsets for use in the method for FIGS. 8A-8B;

FIGS. 11A-11F are flow diagrams showing the routine for testingthreshold limits for use in the method of FIGS. 8A-8B;

FIG. 12 is a flow diagram showing the routine for evaluating the onset,progression, regression, and status quo of myocardial ischemia for usein the method of FIGS 8A-8B;

FIGS. 13A-13D are flow diagrams showing the routine for determining anonset of myocardial ischemia for use in the routine of FIG. 12;

FIGS. 14A-14D are flow diagrams showing the routine for determiningprogression or worsening of myocardial ischemia for use in the routineof FIG. 12;

FIGS 15A-15D are slow diagrams showing the routine for determiningregression or improving of myocardial ischemia for use in the routine ofFIG. 12; and

FIG. 16 is a flow diagram showing the routine for determining thresholdstickiness (“hysteresis”) for use in the method of FIG. 12.

DETAILED DESCRIPTION

FIG. 1 is a block diagram showing an automated collection and analysispatient care system 10 for diagnosing and monitoring myocardial ischemiain accordance with the present invention. An exemplary automatedcollection and analysis patient care system suitable for use with thepresent invention is disclosed in the related, commonly-owned U.S. Pat.No. 6,312, 378, issued Nov. 6, 2001, the disclosure of which isincorporated herein by reference. Preferably, an individual patient 11is a recipient of an implantable medical device 12, such as, by way ofexample, an IPG, cardiovascular or heart failure monitor, or therapeuticdevice, with a set of leads extending into his or her heart andelectrodes implanted throughout the cardiopulmonary system.Alternatively, an external monitoring or therapeutic medical device 26,a subcutaneous monitor or device inserted into other organs, a cutaneousmonitor, or even a manual physiological measurement device, such as anelectrocardiogram or heart rate monitor, could be used. The implantablemedical device 12 and external medical device 26 include circuitry forrecording into a short-term, volatile memory telemetered signals storedfor later retrieval, which become part of a set of device and derivedmeasures, such as described below, by way of example, with reference toFIG. 2. Exemplary implantable medical devices suitable for use in thepresent invention include the Discovery line of pacemakers, manufacturedby Guidant Corporation, Indianapolis, Ind., and the Gem line of ICDs,manufactured by Medtronic Corporation, Minneapolis, Minn.

The telemetered signals stored in the implantable medical device 12 arepreferably retrieved upon the completion of an initial observationperiod and subsequently thereafter on a continuous, periodic (daily)basis, such as described in the related, commonly-owned U.S. Pat. No.6,221,011, issued Apr. 24, 2001, the disclosure of which is incorporatedherein by reference. A programmer 14, personal computer 18, or similardevice for communicating with an implantable medical device 12 can besued to retrieve the telemetered signals. A magnetized reed switch (notshown) within the implantable medical device 12 closes in response tothe placement of a wand 13 over the site of the implantable medicaldevice 12. The programmer 14 sends programming or interrogatinginstructions to and retrieves stored telemetered signals from theimplantable medical device 12 via RF signals exchanged through the wand13. Similar communication means are sued for accessing the externalmedical device 26. Once downloaded, the telemetered signals are sent viaan internetwork 15, such as the Internet, to a server system 16 whichperiodically receives and stores the telemetered signals as devicemeasures in patient care records 23 in a database 17, as furtherdescribed below, by way of example, with reference to FIGS. 2 and 3. Anexemplary programmer 14 suitable for use in the present invention is theModel 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind.

The patient 11 is remotely monitored by the server system 16 via theinternetwork 15 through the periodic receipt of the retrieved devicemeasures from the implantable medical device 12 or external medicaldevice 26. The patient care records 23 in the database 17 are organizedinto two identified sets of device measures: an optional referencebaseline 26 recorded during an initial observation period and monitoringsets 27 recorded subsequently thereafter. The device measures sets areperiodically analyzed and compared by the server system 16 to indicatorthresholds corresponding to quantifiable physiological measures of apathophysiology indicative of myocardial ischemia, as further describedbelow with reference to FIG. 5. As necessary, feedback is provided tothe patient 11. By way of example, the feedback includes an electronicmail message automatically sent by the server system 16 over theinternetwork 15 to a personal computer 18 (PC) situated for local accessby the patient 11. Alternatively, the feedback can be sent through atelephone interface device 19 as an automated voice mail message to atelephone 21 or as an automated facsimile message to a facsimile machine22, both also situated for local access by the patient 11. Moreover,simultaneous notifications can also be delivered to the patient'sphysician, hospital, or emergency medical services provider 29 usingsimilar feedback means to deliver the information.

The server system 10 can consist of either a single computer system or acooperatively networked or clustered set of computer systems. Eachcomputer system is a general purpose, programmed digital computingdevice consisting of a central processing unit (CPU), random accessmemory (RAM), non-volatile secondary storage, such as a hard drive or CDROM drive, network interfaces, and peripheral devices, including userinterfacing means, such as a keyboard and display. Program code,including software programs, and data are loaded into the RAM forexecution and processing by the CPU and results are generated fordisplay, output, transmittal, or storage, as is known in the art.

The database 17 stores patient care records 23 for each individualpatient to whom remote patient care is being provided. Each patient carerecord 23 contains normal patient identification and treatment profileinformation, as well as medical history, medications taken, height andweight, and other pertinent data (not shown). The patient care records23 consist primarily of two sets of data: device and derived measures(D&DM) sets 24 a, 24 b and quality of life (QOL) sets 25 a, 25 b, theorganization of which are further described below with respect to FIGS.2 and 3, respectively. The device and derived measures sets 24 a, 24 band quality of life and symptom measures sets 25 a, 25 b can be furtherlogically categorized into two potentially overlapping sets. Thereference baseline 26 is a special set of device and derived referencemeasures sets 24 a and quality of life and symptom measures sets 25 arecorded and determined during an initial observation period. Monitoringsets 27 are device and derived measures sets 24 b and quality of lifeand symptom measures sets 25 b recorded and determined thereafter on aregular, continuous basis. Other forms of database organization arefeasible.

The implantable medical device 12 and, in a more limited fashion, theexternal medical device 26, record patient information for care ofpatients with myocardial ischemia on a regular basis. The recordedpatient information is downloaded and stored in the database 17 as partof a patient care record 23. Further patient information can be derivedfrom recorded data, as is known in the art. FIG. 2 is a database schemashowing, by way of example, the organization of a device and derivedmeasures set record 40 for patient care stored as part of a patient carerecord in the database 17 of the system of FIG. 1. Each record 40 storespatient information which includes a snapshot of telemetered signalsdata which were recorded by the implantable medical device 12 or theexternal medical device 26, for instance, on per heartbeat, binnedaverage or derived bases; measures derived from the recorded devicemeasures; and manually collected information, such as obtained through apatient medical history interview or questionnaire. The followingnon-exclusive information can be recorded for a patient: atrialelectrical activity 41, ventricular electrical activity 42, PR intervalor AV interval 43, QRS measures 44, ST segment measures 45, T wavemeasures 46, PT interval 47, body temperature 48, posture 49,cardiovascular pressures 50, pulmonary artery diastolic pressure measure51, cardiac output 52, systemic blood pressure 53, patient geographiclocation (altitude) 54, mixed venous oxygen score 55, arterial oxygenscore 56, pulmonary measures 57, minute ventilation 58, potassium [K+]level 59, sodium [Na+] level 60, glucose level 61, blood urea nitrogen(BUN) and creatinine 62, acidity (pH) level 63, hematocrit 64, hormonallevels 65, cardiac injury chemical tests 66, serum creatinine kinase 67,serum troponin 68, left ventricular wall motion changes 69, myocardialblood flow 70, coronary sinus lactate production 71, cardiac injurychemical tests 72, central nervous system (CNS) injury chemical tests73, central nervous system blood flow 74, interventions made by theimplantable medical device or external medical device 75, and therelative success of any interventions made 76. In addition, theimplantable medical device or external medical device communicatesdevice-specific information, including battery status, general devicestatus and program settings 77 and the time of day 78 for the variousrecorded measures. Other types of collected, recorded, combined, orderived measures are possible, as is known in the art.

The device and derived measures sets 24 a, 24 b (shown in FIG. 1), alongwith quality of life and symptom measures sets 25 a, 25 b, as furtherdescribed below with reference to FIG. 3, are continuously andperiodically received by the server system 16 as part of the on-goingpatient care monitoring and analysis function. These regularly collecteddata sets are collectively categorized as the monitoring sets 27 (shownin FIG. 1). In addition, select device and derived measures sets 24 aand quality of life and symptom measures sets 25 a can be designated asa reference baseline 26 at the outset of patient care to improve theaccuracy and meaningfulness of the serial monitoring sets 27. Selectpatient information is collected, recorded, and derived during aninitial period of observation or patient care, such as described in therelated, commonly-owned U.S. Pat. NO. 6,221,011, issued Apr. 24, 2001,the disclosure of which is incorporated herein by reference.

As an adjunct to remote patient care through the monitoring of measuredphysiological data via the implantable medical device 12 or externalmedical device 26, quality of life and symptom measures sets 25 a canalso be stored in the database 17 as part of the reference baseline 26,if used, and the monitoring sets 27. A quality of life measure is asemi-quantitative self-assessment of an individual patient's physicaland emotional well-being and a record of symptoms, such as provided bythe Duke Activities Status Indicator. These scoring systems can beprovided for use by the patient 11 on the personal computer 18 (shown inFIG. 1) to record his or her quality of life scores for both initial andperiodic download to the server system 16. FIG. 3 is a database schemashoeing, by way of example, the organization of a quality of life record80 for use in the database 17. The following information is recorded fora patient: overall health wellness 81, psychological state 82,activities of daily living 83, work status 84, geographic location 85,family status 86, shortness of breath 87, energy level 88, exercisetolerance 89, chest discomfort 90, nausea 91, diaphoresis 92, and timeof day 93, and other quality of life and symptom measures as would beknown to one skilled in the art.

The patient may also add non-device quantitative measures, such as thesix-minute walk distance, as complementary data to the device andderived measures sets 24 a, 24 b and the symptoms during the six-minutewalk to quality of life and symptom measures sets 25 a, 25 b.

Other types of quality of life and symptom measures are possible, suchas those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452-454, W. B. Saunders Co.(1997), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, for angina alsodescribed in Ibid.

On a periodic basis, the patient information stored in the database 17is analyzed and compared to pre-determined cutoff levels, which, whenexceeded, can provide etiological indications of myocardial ischemiasymptoms. FIG. 4 is a database schema showing, by way of example, theorganization of a combined measures set record 95 for use in thedatabase 17. Each record 95 stores patient information obtained orderived from the device and derived measures sets 24 a, 24 b and qualityof life and symptom measures sets 25 a, 25 b as maintained in thereference baseline 26, if used, and the monitoring sets 27. The combinedmeasures set 95 represents those measures most (but not exhaustively orexclusively) relevant to a pathophysiology indicative of myocardialischemia and are determined as further described below with reference toFIGS. 8A-8B. The following information is stored for a patient: heartrate 96, heart rhythm (e.g., normal sinus vs. atrial fibrillation) 97,ST segment elevation 98, ST segment depression 99, T wave inversion(including changes) 100, pacing modality 101, pulmonary artery diastolicpressure 102, cardiac output 103, arterial oxygen score 104, mixedvenous oxygen score 105, respiratory rate 106, transthoracic impedance107, pressures 108, left ventricular wall motion changes 109, myocardialblood flow 110, coronary sinus lactate production 111, myocardial bandcreatinine kinase levels 112, troponin levels 113, patient activityscore 114, posture 105, exercise tolerance quality of life and symptommeasures 116, respiratory distress quality of life and symptom measures117, chest discomfort quality of life and symptom measures 118, anyinterventions made to treat myocardial ischemia 119, including treatmentby medical device, via drug infusion administered by the patient or by amedical device, surgery, and any other form of medical intervention asis known in the art, the relative success of any such interventions made120, and time of day 121. Other types of comparison measures regardingmyocardial ischemia are possible as is known in the art. In thedescribed embodiment, each combined measures set 95 is sequentiallyretrieved from the database 17 and processed. Alternatively, eachcombined measures set 95 could b e stored within a dynamic datastructure maintained transitorily in the random access memory of theserver system 16 during the analysis and comparison operations.

FIG. 5 is a block diagram showing the software modules of the serversystem 16 of the system 10 of FIG. 1. Each module is a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU of the server system 16 as object or byte code, as is known inthe art. The various implementations of the source code and object andbyte codes can be held on a computer-readable storage medium or embodiedon a transmission medium in a carrier wave. The server system 16includes three primary software modules, database module 125, diagnosticmodule 126, and feedback module 128, which perform integrated functionsas follows.

First, the database module 125 organizes the individual patient carerecords 23 stored in the database 17 (shown in FIG. 1) and efficientlystores and accesses the reference baseline 26, monitoring sets 27, andpatient care data maintained in those records. Any type of databaseorganization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

Next, the diagnostic module 126 makes findings of myocardial ischemiabased on the comparison and analysis of the data measures from thereference baseline 26 and monitoring sets 27. The diagnostic moduleincludes three modules: comparison module 130, analysis module 131, andquality of life module 132. The comparison module 130 compares recordedand derived measures retrieved from the reference baseline 26, if used,and monitoring sets 27 to indicator thresholds 129. The database 17stores individual patient care records 23 for patients suffering fromvarious health disorders and diseases for which they are receivingremote patient care. For purposes of comparison and analysis by thecomparison module 130, these records can be categorized into peer groupscontaining the records for those patients suffering from similardisorders, as well as being viewed in reference to the overall patientpopulation. The definition of the peer group can be progressivelyrefined as the overall patient population grows. To illustrate, FIG. 6is a record view showing, by way of example, a set of partial patientcare records for care of patients with myocardial ischemia stored in thedatabase 17 for three patients, Patient 1, Patient 2, and Patient 3. Foreach patient, three sets of peer measures, X, Y, and Z, are shown. Eachof the measures, X, Y, and Z, could be either collected or derivedmeasures from the reference baseline 26, if used, and monitoring sets27.

The same measures are organized into time-based sets with Set 0representing sibling measures made at a reference time t=0. Similarly,Set n−2, Set n−1 and Set n each represent sibling measures made at laterreference times t=n−2, t=n−1 and t=n, respectively. Thus, for a givenpatient, such as Patient 1, serial peer measures, such as peer measureX₀ through X_(n), represent the same type of patient informationmonitored over time. The combined peer measures for all patients can becategorized into a health disorder- or disease-matched peer group. Thedefinition of disease-matched peer group is a progressive definition,refined overtime as the number of monitored patients grows. Measuresrepresenting different types of patient information, such as measuresX₀, Y₀, and Z₀, are sibling measures. These are measures which are alsomeasured over time, but which might have medially significant meaningwhen compared to each other within a set for an individual patient.

The comparison module 130 performs two basic forms of comparisons.First, individual measures for a given patient can be compared to otherindividual measures for that same patient (self-referencing). Thesecomparisons might be peer-to-peer measures, that is, measures relatingto a one specific type of patient information, projected over time, forinstance, X_(n), X_(n−1), X_(n−2), . . . X₀, or sibling-to-siblingmeasures, that is, measures relating to multiple types of patientinformation measured during the same time period, for a single snapshot,for instance, X_(n), Y_(n), and Z_(n), or projected over time, forinstance, X_(n), Y_(n), Z_(n), X_(n−1), Y_(n−1), Z_(n−1), X_(n−2),Y_(n−2), Z_(n−2), . . . X₀, Y₀, Z₀. Second, individual measures for agiven patient can be compared to other individual measures for a groupof other patients sharing the same disorder- or disease-specificcharacteristics (peer group referencing) or to the patient population ingeneral (population referencing). Again, these comparisons might bepeer-to-per measures projected over time, for instance, X_(n), X_(n′),X_(″), X_(n−1), X_(n−1′), X_(n−1″), X₂, X_(n−2′), X_(n−2″) . . . X₀,X_(′), X_(0″), or comparing the individual patient's measures to anaverage from the group. Similarly, these comparisons might besibling-to-sibling measures for single snapshots, for instance, X_(n),X_(n′), X_(n″), Y_(n), Y_(n′)Y_(n″), and Z_(n), Z_(n′), Z_(n″), orprojected over time, for instance, X_(m), X_(n′), X_(n″), Y_(n), Y_(′),Y_(n″), Z_(n), Z_(n′), Z_(n″), X_(n−1), X_(n−1′), X_(n−1″), Y_(n−1),Y_(n−1′), Y_(n−1″), X_(n−1), Z_(n−1), Z_(n−1′), Z_(n−1″), X_(n−2),X_(n−2′), X_(n−2″), Y_(n−2), Y_(n−2′), Y_(n−2″), Y_(n−2), Y_(n−2′),Y_(n−2″), Z_(n−2), Z_(n−2′), Z_(n−2″) . . . X₀, X_(0′), X_(0″), Y₀,Y_(0′), Y_(0″), and Z₀, Z_(0′), Z_(0″). Other forms of comparisons arefeasible, including multiple disease diagnoses for diseases exhibitingsimilar physiological measures or which might be a secondary diseasecandidate. Other forms of comparisons are feasible, including multipledisease diagnoses for diseases exhibiting similar abnormalities inphysiological measures that might result from a second disease butmanifest in different combinations or onset in different temporalsequences.

FIG. 7 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records 23 of FIG. 1. Each patient carerecord 23 includes characteristics data 350, 351, 352, includingpersonal traits, demographics, medical history, and related personaldata, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 350 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40-45; ademographic of resident of New York City; and a medical historyconsisting of anterior myocardial infraction, congestive heart failureand diabetes. Similarly, the characteristics data 351 for patient 2might include identical personal traits, thereby resulting in partialoverlap 353 of characteristics data 350 and 351. Similar characteristicsoverlap 354, 355, 356 can exist between each respective patient. Theoverall patient population 357 would include the universe of allcharacteristics data. As the monitoring population grows, the number ofpatients with personal traits matching those of the monitored patientwill grow, increasing the value of peer group referencing. Large peergroups, well matched across all monitored measures, will result in awell known natural history of disease and will allow for more accurateprediction of the clinical course of the patient being monitored. If thepopulation of patients is relatively small, only some traits 356 will beuniformly present in any particular peer group. Eventually, peer groups,for instance, composed of 100 or more patients each, would evolve underconditions in which there would be complete overlap of substantially allsalient data, thereby forming a powerful core reference group for anynew patient being monitored.

Referring back to FIG. 5, the analysis module 131 analyzes the resultsfrom the comparison module 130, which are stored as a combined measuresset 95 (not shown), to a set of indicator thresholds 129, as furtherdescribed below with reference to FIGS. 8A-8B. Similarly, the quality oflife module 132 compares quality of life and symptom measures 25 a, 25 bfrom the reference baseline 26 and monitoring sets 27, the results ofwhich are incorporated into the comparisons performed by the comparisonmodule 13, in part, to either refute or support the findings based onphysiological “hard” data. Finally, the feedback module 128 providesautomated feedback to the individual patient based, in part, on thepatient status indicator 127 generated by the diagnostic module 126. Asdescribed above, the feedback could be by electronic mail or byautomated voice mail or facsimile. The feedback can also includenormalized voice feedback, such as described in the related,commonly-owned U.S. Pat. No. 6,203,495, issued Mar. 20, 2001, thedisclosure of which is incorporated herein by reference. In addition,the feedback module 128 determines whether any changes to interventivemeasures are appropriate based on threshold stickiness (“hysteresis”)133, as further described below with reference to FIG. 15. The thresholdstickiness 133 can prevent fickleness in diagnostic routines resultingfrom transient, non-trending and non-significant fluctuations in thevarious collected and derived measures in favor of more certainty indiagnosis. In a further embodiment of the present invention, thefeedback module 128 includes a patient query engine 134 which enablesthe individual patient 11 to interactively query the server system 16regarding the diagnosis, therapeutic maneuvers, and treatment regimen.Conversely, the patient query engines 134 can be found in interactiveexpert systems for diagnosing medical conditions can interactively querythe patient. Using the personal computer 18 (shown in FIG. 1), thepatient can have an interactive dialogue with the automated serversystem 16, as well as human experts as necessary, to self assess his orher medical condition. Such expert systems are well known in the art, anexample of which is the MYCIN expert system developed at StanfordUniversity and described in Buchanan, B. & Shortlife, E., “RULE-BASEDEXPERT SYSTEMS. The MYCIN Experiments of the Stanford HeuristicProgramming Project,” Addison-Wesley (1984). The various forms offeedback described above help to increase the accuracy and specificityof the reporting of the quality of life and symptomatic measures.

FIGS. 8A-8B are flow diagrams showing a method for diagnosing andmonitoring myocardial ischemia and outcomes thereof 135 using anautomated collection and analysis patient care system 10 in accordancewith the present invention. First, the indicator thresholds 129 (shownin FIG. 5) are set (block 136) by defining a quantifiable physiologicalmeasure of a pathophysiology indicative of myocardial ischemia andrelating to the each type of patient information in the combined deviceand derived measures set 95 (shown in FIG. 4). The actual values of eachindicator threshold can be finite cutoff values, weighted values orstatistical ranges, as discussed below with reference to FIGS. 11A-11F.Next, the reference baseline 26 (block 137) and monitoring sets 27(block 138) are retrieved rom the database 17, as further describedbelow with reference to FIGS. 9 and 10, respectively. Each measure inthe combined device and derived measures set 95 is tested against thethreshold limits defined for each indicator threshold 129 (block 139),as further described below with reference to FIGS. 11A-11F. Thepotential onset, progression, regression, or status quo of myocardialischemia is then evaluated (block 140) based upon the findings of thethreshold limits tests (block 139), as further described below withreference to FIGS. 13A-13D, 14A-14D, 15A-15D.

In a further embodiment, multiple near-simultaneous disorders areconsidered in addition to primary myocardial ischemia. Primarymyocardial ischemia is defined as the onset or progression of myocardialischemia without obvious inciting cause. Secondary myocardial ischemiais defined as the onset or progression of myocardial ischemia (in apatient with or without pre-existing myocardial ischemia) from anotherdisease process, such as coronary insufficiency, respiratoryinsufficiency, atrial fibrillation, and so forth. Other health disordersand diseases can potentially share the same forms of symptomatology asmyocardial ischemia, such as congestive heart failure, respiratoryinsufficiency, pneumonia, exacerbation of chronic bronchitis, renalfailure, sleep-apnea, stroke, anemia, atrial fibrillation, other cardiacarrhythmias, and so forth. If more than one abnormality is present, therelative sequence and magnitude of onset of abnormalities in themonitored measures becomes most important in sorting and prioritizingdisease diagnosis and treatment.

Thus, if other disorders or diseases are being cross-referenced anddiagnosed (block 141), their status is determined (block 142). In thedescribed embodiment, the operations of ordering and prioritizingmultiple near-simultaneous disorders (box 151) by the testing ofthreshold limits and analysis in a manner similar to congestive heartfailure as described above, preferably in parallel to the presentdetermination, is described in the related, commonly-owned U.S. Pat. No.6,440,066, issued Aug. 27, 2002, the disclosure of which is incorporatedherein by reference. If myocardial ischemia is due to an obviousinciting cause, i.e., secondary myocardial ischemia, (block 143), anappropriate treatment regimen for myocardial ischemia as exacerbated byother disorders is adopted that includes treatment of secondarydisorders, e.g., myocardial ischemia, respiratory insufficiency, atrialfibrillation, and so forth (block 144) and a suitable patient statusindicator 127 for myocardial ischemia is provided (block 146) to thepatient. Suitable devices and approaches to diagnosing and treatingcongestive heart failure, respiratory distress and atrial fibrillationare described in related, commonly-owned U.S. Pat. No. 6,336,903, issuedJan. 8, 2002; U.S. Pat. No. 6,398,728, issued Jun. 4, 2002; and U.S.Pat. No. 6,411,840, issued Jun. 25, 2002, the disclosures of which areincorporated herein by reference.

Otherwise, if primary myocardial ischemia is indicated (block 143), aprimary treatment regimen is followed (block 145). A patient statusindicator 127 for myocardial ischemia is provided (block 146) to thepatient regarding physical well-being, disease prognosis, including anydeterminations of disease onset, progression, regression, or status quo,and other pertinent medical and general information of potentialinterest to the patient.

Finally, in a further embodiment, if the patient submits a query to theserver system 16 (block 147), the patient query is interactivelyprocessed by the patient query engine (block 148). Similarly, if theserver elects to query the patient (block 149), the server query isinteractively processed by the server query engine (block 150). Themethod then terminates if no further patient or server queries aresubmitted.

FIG. 9 is a flow diagram showing the routine for retrieving referencebaseline sets 137 for use in the method of FIGS. 8A-8B. The purpose ofthis routine is to retrieve the appropriate reference baseline sets 26,if used, from the database 17 based on the types of comparisons beingperformed. First, if the comparisons are self referencing with respectto the measures stored in the individual patient care record 23 (block152), the reference device and derived measures set 24 a and referencequality of life and symptom measures set 25 a, if used, are retrievedfor the individual patient from the database 17 (block 153). Next, ifthe comparisons are peer group referencing with respect to measuresstored in the patient care records 23 for a health disorder- ordisease-specific peer group (block 154), the reference device andderived measures set 24 a and reference quality of life and symptommeasures set 25 a, if sued, are retrieved from each patient care record23 for the peer group from the database 17 (block 155). Data for eachmeasure (e.g., minimum, maximum, averaged, standard deviation (SD), andtrending data) from the reference baseline 26 for the peer group is thencalculated (block 156). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 157), the reference deviceand derived measures set 24 a and reference quality of life and symptommeasures set 25 a, if used, are retrieved from each patient care record23 from the database 17 (block 158). Minimum, maximum, averaged,standard deviation, and trending data and other numerical processesusing the data, as is known in the art, for each measure from thereference baseline 26 for the peer group is then calculated (block 159).The routine then returns.

FIG. 10 is a flow diagram showing the routine for retrieving monitoringsets 138 for use in the method of FIGS. 8A-8B. The purpose of thisroutine is to retrieve the appropriate monitoring sets 27 from thedatabase 17 based on the types of comparisons being performed. First, ifthe comparisons are self referencing with respect to the measures storedin the individual patient care record 23 (block 160), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved for the individual patient from thedatabase 17 (block 161). Next, if the comparisons are peer groupreferencing with respect to measures stored in the patient care records23 for a health disorder- or disease-specific peer group (block 162),the device and derived measures set 24 b and quality of life and symptommeasures set 25 b, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 163). Data for eachmeasure (e.g., minimum, maximum, averaged, standard deviation, andtrending data) from the monitoring sets 27 for the peer group is thencalculated (block 164). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 165), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved from each patient care record 23 from thedatabase 17 (block 166). Minimum, maximum, averaged, standard deviation,and trending data and other numerical processes using the data, as isknown in the art, for each measure from the monitoring sets 27 for thepeer group is then calculated (block 167). The routine then returns.

FIGS. 11A-11F are flow diagrams showing the routine for testingthreshold limits 139 for use in the method of FIGS. 8A and 8B. Thepurpose of this routine is to analyze, compare, and log any differencesbetween the observed, objective measures stored in the referencebaseline 26, if used, and the monitoring sets 27 to the indicatorthresholds 129. Briefly, the routine consists of tests pertaining toeach of the indicators relevant to diagnosing and monitoring myocardialischemia. The threshold tests focus primarily on: (1) changes to andrates of change for the indicators themselves, as stored in the combineddevice and derived measures set 95 (shown in FIG. 4) or similar datastructure; and (2) violations of absolute threshold limits which triggeran alert. The timing and degree of change may vary with each measure andwith the natural fluctuations noted in that measure during the referencebaseline period. In addition, the timing and degree of change might alsovary with the individual and the natural history of a measure for thatpatient.

One suitable approach to performing the threshold tests uses a standardstatistical linear regression technique using a least squares error fit.The least squares error fit can be calculated as follows:$\begin{matrix}{y = {\beta_{0} + {\beta_{1}x}}} & (1) \\{\beta = \frac{{SS}_{xy}}{{SS}_{xx}}} & (2) \\{{SS}_{xy} = {{\sum\limits_{i = 1}^{n}\quad{x_{i}y_{i}}} - \frac{\left( {\sum\limits_{i = 1}^{n}\quad x_{i}} \right)\left( {\sum\limits_{i = 1}^{n}\quad y_{i}} \right)}{n}}} & (3) \\{{SS}_{xx} = {{\sum\limits_{i = 1}^{n}\quad x_{i}^{2}} - \frac{\left( {\sum\limits_{i = 1}^{n}\quad x_{i}} \right)^{2}}{n}}} & (4)\end{matrix}$where n is the total number of measures, x_(i) is the time of day formeasure i, and y_(i) is the value of measure i, β₁ is the slope, and β₀is the y-intercept of the least squares error line. A positive slope β₁indicates an increasing trend, a negative slope β₁ indicates adecreasing trend, and no slope indicates no change in patient conditionfor that particular measure. A predicted measure value can be calculatedand compared to the appropriate indicator threshold 129 for determiningwhether the particular measure has either exceeded an acceptablethreshold rate of change or the absolute threshold limit.

For any given patient, three basic types of comparisons betweenindividual measures stored in the monitoring sets 27 are possible: selfreferencing, peer group, and general population, as explained above withreference to FIG. 6. In addition, each of these comparisons can includecomparisons to individual measures stored in the pertinent referencebaselines 24.

The indicator thresholds 129 for detecting a trend indicatingprogression into a state of myocardial ischemia or a state of imminentor likely myocardial ischemia, for example, over a one week time period,can be as follows:

-   -   (1) Heart rate (block 170): If the heart rate has increased over        1.0 SD from the mean heart rate in the reference baseline 26        (block 171), the increased heart rate and time span over which        it occurs are logged in the combined measures set 95 (block        172).    -   (2) ST segment elevation (block 173): If the ST segment        elevation on the electrocardiogram has increased over 1.0 SD        from the mean ST segment elevation level in the reference        baseline 26 (block 174), the increased ST segment elevation and        time span over which it occurs are logged in the combined        measures set 95 (block 175).    -   (3) ST segment depression (block 176): If the ST segment        depression on the electrocardiogram has increased over 1.0 SD        from the mean ST segment depression level in the reference        baseline 26 (block 177), the increased ST segment depression and        time span are logged in the combined measures set 95 (block        178).    -   (4) Creatinine kinase (block 179): If the myocardial creatinine        kinase has increased over 1.0 SD from the mean myocardial        creatinine kinase in the reference baseline 26 (block 180), the        increased myocardial creatinine kinase and time span are logged        in the combined measures set 95 (block 181).    -   (5) Troponin (block 182): If the myocardial troponin has        increased over 1.0 SD from the mean myocardial troponin in the        reference baseline 26 (block 183), the increased myocardial        troponin and time span are logged in the combined measures set        95 (block 184).    -   (6) Coronary sinus lactate (block 185): If the coronary sinus        lactate has increased over 1.0 SD from the mean coronary sinus        lactate in the reference baseline 26 (block 186), the increased        coronary sinus lactate and time span are logged in the combined        measures set 95 (block 187).    -   (7) Myocardial blood flow (block 188): If the myocardial blood        flow has decreased over 1.0 SD from the mean myocardial blood        flow in the reference baseline 26 (block 189), the decreased        myocardial blood flow and time span are logged in the combined        measures set 95 (b lock 190).    -   (8) Cardiac output (block 191): If the cardiac output has        decreased over 1.0 SD from the mean cardiac output in the        reference baseline 26 (block 192), the decreased cardiac output        and time span are logged in the combined measures set 95 (block        193).    -   (9) Pulmonary artery diastolic pressure (PADP) (block 194)        reflects left ventricular filling pressure and is a measure of        left ventricular dysfunction. Ideally, the left ventricular end        diastolic pressure (LVEDP) should be monitored, but in practice        is difficult to measure. Consequently, without the LVEDP, the        PADP, or derivatives thereof, are suitable for use as an        alternative to LVEDP in the present invention. If the PADP has        increased over 1.0 SD from the mean PADP in the reference        baseline 26 (block 195), the increased PADP and time span over        which that increase occurs are logged in the combined measures        set 95 (block 196). Other cardiac pressures or derivatives could        also apply.    -   (10) Myocardial wall motion (block 197): If the myocardial wall        motion has decreased over 1.0 SD from the mean myocardial wall        motion in the reference baseline 26 (block 198), the decreased        myocardial wall motion and time span are logged in the combined        measures set 95 (block 199).    -   (11) Patient activity score (block 200): If the mean patient        activity score has decreased over 1.0 SD from the mean patient        activity score in the reference baseline 26 (block 201), the        decreased patient activity score and time span are logged in the        combined measures set 95 (block 202).    -   (12) Exercise tolerance quality of life (QOL) measures (block        203): If the exercise tolerance QOL has decreased over 1.0 SD        from the mean exercise tolerance in the reference baseline 26        (block 204), the decrease in exercise tolerance and the time        span over which it occurs are logged in the combined measures        set 95 (block 205).    -   (13) Respiratory distress quality of life (QOL) measures (block        206): If the respiratory distress QOL measure has deteriorated        by more than 1.0 SD from the mean respiratory distress QOL        measure in the reference baseline 26 (block 207), the increase        in respiratory distress and the time span over which it occurs        are logged in the combined measures set 95 (block 208).    -   (14) QRS duration (block 209): The presence or absence of a QRS        duration greater than or equal to 120 ms, like left bundle        branch block, is determined and, if present (block 210), a        prolonged QRS duration is logged (block 211).    -   (15) Atrial fibrillation (block 212): The presence or absence of        atrial fibrillation (AF) is determined and, if present (block        213), atrial fibrillation is logged (block 214).    -   (16) Rhythm changes (block 215): The type and sequence of        conduction abnormalities and rhythm measure changes is        significant and is determined based on the timing of the        relevant QRS duration measure and the relevant rhythm measure,        such as sinus rhythm. For instance, a finding that the QRS        duration has suddenly broadened may make relying upon ST segment        or T wave changes as markers of myocardial ischemia difficult.        Similarly, a rhythm measure change to atrial fibrillation may        precipitate myocardial ischemia but rhythm measure changes        should indicate therapy directions against atrial fibrillation        rather than the primary onset of myocardial ischemia. Thus, if        there are QRS duration changes and/or rhythm measure changes        (block 216), the sequence of the QRS duration changes and the        rhythm measure changes and associated time spans are logged        (block 217).

Note also that an inversion of the indicator thresholds 129 definedabove could similarly be used for detecting a trend in diseaseregression. One skilled in the art would recognize that these measureswould vary based on whether or not they were recorded during rest orduring activity and that the measured activity score can be used toindicate the degree of patient rest or activity. The patient activityscore can be determined via an implantable motion detector, for example,as described in U.S. Pat. NO. 4,428,378, issued Jan. 31, 1984, toAnderson et al., the disclosure of which is incorporated herein byreference.

The indicator thresholds 129 for detecting a trend towards a state ofmyocardial ischemia can also be used to declare, a priori, myocardialischemia present, regardless of pre-existing trend data when certainlimits are established, such as:

-   -   (1) An absolute limit of ST segment elevation (block 173)        exceeding 2.0 mm in the absence of a QRS duration greater than        or equal to 120 ms is an a priori definition of myocardial        ischemia.    -   (2) An absolute limit of myocardial band creatinine kinase mass        (block 179) above 5 ng/ml is an a priori definition of        myocardial ischemia.    -   (3) An absolute limit of troponin-I (block 182) above 0.5 ng/ml        is an a priori definition of myocardial ischemia.

FIG. 12 is a flow diagram showing the routine for evaluating the onset,progression, regression and status quo of myocardial ischemia 140 foruse in the method of FIGS. 8A and 8B. The purpose of this routine is toevaluate the presence of sufficient indicia to warrant a diagnosis ofthe onset, progression, regression, and status quo of myocardialischemia. Quality of life and symptom measures 25 a, 25 b can beincluded in the evaluation (block 230) by determining whether any of theindividual quality of life and symptom measures 25 a 25 b have changedrelative to the previously collected quality of life and symptommeasures from the monitoring sets 27 and the reference baseline 26, ifused. For example, an increase in the shortness of breath measure 87 andexercise tolerance measure 89 would corroborate a finding of myocardialischemia. Similarly, a transition from NYHA Class II angina to NYHAClass III angina would indicate a deterioration or, conversely, atransition from NYHA Class III to NYHA Class II angina status wouldindicate improvement or progress. Incorporating the quality of life andsymptom measures 25 a, 25 b into the evaluation can help, in part, torefute or support findings based on physiological data. Next, adetermination as to whether any changes to interventive measures areappropriate based on threshold stickiness (“hysteresis”) is made (block231), as further described below with reference to FIG. 15.

The routine returns upon either the determination of a finding orelimination of all factors as follows. If a finding of myocardialischemia was not previously diagnosed (block 232), a determination ofdisease onset is made (block 233), as further described below withreference to FIGS. 13A-13C. Otherwise, if myocardial ischemia waspreviously diagnosed (block 232), a further determination of eitherdisease progression or worsening (block 234) or regression or improving(block 235) is made, as further described below with reference to FIGS.14A-14C and 15A-15C, respectively. If , upon evaluation, neither diseaseonset (block 233), worsening (block 234) or improving (block 235) isindicated, a finding of status quo is appropriate (block 236) and noted(block 237). Otherwise, myocardial ischemia and the related outcomes areactively managed (block 238) through the administration of,non-exclusively, anticoagulation, antiplatelet drugs, beta-blockade,coronary vasodilators, afterload reduction, lipid lowering drugs,electrical therapies, mechanical therapies, and other therapies as areknown in the art. The management of myocardial ischemia is described, byway of example, in E. Braunwald, ed., “Heart Disease—A Textbook ofCardiovascular Medicine,” Chs. 35-38, W. B. Saunders Co. (1997), thedisclosure of which is incorporated herein by reference. The routinethen returns.

FIGS. 13A-13D are flow diagrams showing the routine for determining anonset of myocardial ischemia 233 for use in the routine of FIG. 12.Myocardial ischemia is possible based on three general symptomcategories: reduced exercise capacity (block 244), respiratory distress(block 251), and increased chest discomfort (angina) (block 261). Aneffort is made to diagnose myocardial ischemia manifesting primarily asresulting in reduced exercise capacity (block 244), increasedrespiratory distress (block 251) and/or angina (block 261). Increasedchest discomfort, or angina, can be a direct marker of myocardialischemia. Reduced exercise capacity generally serves as a marker of lowcardiac output and respiratory distress as a marker of increased leftventricular end diastolic pressure. Both reduced exercise capacity andrespiratory distress may result from a myocardial wall motionabnormality that occurs in response to myocardial ischemia. The clinicalaspects of acute myocardial ischemia (and infarction) are described, byway of example, in E. Braunwald, ed., “Heart Disease—A Textbook ofCardiovascular Medicine,” Chs 1 and 36-38, W. B. Saunders Co. (1997),the disclosure of which is incorporated herein by reference.

As primary cardiac disease considerations, multiple individualindications (blocks 240-243, 245-250, 252-260) should be present for thethree respective principal symptom findings of myocardial ischemiarelated reduced exercise capacity (block 244), myocardial ischemiarelated respiratory distress (block 251), or myocardial ischemia relatedchest discomfort (block 261), to be indicated, both for disease onset ordisease progression. The presence of primary key findings alone can besufficient to indicate an onset of myocardial ischemia and secondary keyfindings serve to corroborate disease onset. Note the presence of anyabnormality can trigger an analysis for the presence or absence ofsecondary disease processes, such as the presence of atrialfibrillation, pneumonia, or congestive heart failure. Secondary diseaseconsiderations can be evaluated using the same indications (see, e.g.,blocks 141-144 of FIGS. 8A-8B), but with adjusted indicator thresholds129 (shown in FIG. 5) triggered at a change of 0.5 SD, for example,instead of 1.0 SD.

In the described embodiment, the reduced exercise capacity, respiratorydistress, and chest discomfort findings (blocks 244, 251, 261) can beestablished by consolidating the respective individual indications(blocks 240-243, 245-250, 252-260) in several ways. First, in apreferred embodiment, each individual indication (blocks 240-243,245-250, 252-260) is assigned a scaled index value correlating with therelative severity of the indication. For example, ST segment elevation(block 252) could be measured on a scale from ‘1’ to ‘5’ wherein a scoreof ‘1’ indicates no change in ST segment elevation from the referencepoint, a score of ‘2’ a 0.5 SD change, a score of ‘3’a 1.0 SD change, ascore of ‘4’ a 2.0 SD change, and a score of ‘5’ indicates a changeexceeding 3.0 SD. The index value for each of the individual indications(blocks 240-243, 245-250, 252-260) can then either be aggregated oraveraged with a result exceeding the aggregate or average maximumindicating an appropriate myocardial ischemia finding.

Preferably, all scores are weighted depending upon the assignments madefrom the measures in the reference baseline 26. For instance, ST segmentchanges 98, 99 (shown in FIG. 4) could be weighted more heavily thanheart rate 96 if the heart rate in the reference baseline 26 isparticularly high at the outset, making the detection of further diseaseprogression from increases in heart rate, less sensitive. In thedescribed embodiment, ST segment elevation 98 receives the most weightin determining increased chest discomfort whereas new wall motionabnormalities 109 receive the most weight in determining a reducedexercise or respiratory distress finding.

Alternatively, a simple binary decision tree can be utilized whereineach of the individual indications (blocks 240-243, 245-250, 252-260) iseither present or is not present. All or a majority of the individualindications (blocks 240-243, 245-250, 252-260) should be present for therelevant myocardial ischemia finding to be affirmed.

Other forms of consolidating the individual indications (blocks 240-243,245-250, 252-260) are feasible.

FIGS. 14A-14D are flow diagrams showing the routine for determining aprogression or worsening of myocardial ischemia 234 for use in theroutine of FIG. 12. The primary difference between the determinations ofdisease onset, as described with reference to FIGS. 13A-13D, and diseaseprogression is the evaluation of changes indicated in the same factorspresent in a disease onset finding. Thus, a revised myocardial ischemiafinding is possible based on the same three general symptom categories:reduced exercise capacity (block 274), respiratory distress (block 281),and chest discomfort (angina) (block 291). The same factors which needbe indicated to warrant a diagnosis of myocardial ischemia onset areevaluated to determine disease progression.

Similarly, FIGS. 15A-15D are flow diagrams showing the routine fordetermining a regression or improving of myocardial ischemia 235 for usein the routine of FIG. 12. The same factors as described above withreference to FIGS. 13A-13D and 14A-14D, trending in opposite directionsfrom disease onset or progression, are evaluated to determine diseaseregression. As primary cardiac disease considerations, multipleindividual indications (blocks 300-303, 305-310, 312-320) should bepresent for the three principal findings of myocardial ischemia relatedreduced exercise capacity (block 304), myocardial ischemia relatedrespiratory distress (block 311), or myocardial ischemia related chestdiscomfort (block 321), to indicate disease regression.

FIG. 16 is a flow diagram showing the routine for determining thresholdstickiness (“hysteresis”) 231 for use in the method of FIG. 12.Stickiness, also known as hysteresis, is a medical practice doctrinewhereby a diagnosis or therapy will not be changed based upon small ortemporary changes in a patient reading, even though those changes mighttemporarily move into a new zone of concern. For example, if a patientmeasure can vary along a scale of ‘1’ to ‘10’ with ‘10’ being worse, atransient reading of ‘6’, standing alone, on a patient who hasconsistently indicated a reading of ‘5’ for weeks will not warrant achange in diagnosis without a definitive prolonged deterioration firstbeing indicated. Stickiness dictates that small or temporary changesrequire more diagnostic certainty, as confirmed by the persistence ofthe changes, than large changes would require for any of the monitored(device) measures. Stickiness also makes reversal of importantdiagnostic decisions, particularly those regarding life-threateningdisorders, more difficult than reversal of diagnoses of modest import.As an example, automatic external defibrillators (AEDs) manufactured byHeartstream, a subsidiary of Agilent Technologies, Seattle, Wash.,monitor heart rhythms and provide interventive shock treatment for thediagnosis of ventricular fibrillation. Once diagnosis of ventricularfibrillation and a decision to shock the patient has been made, apattern of no ventricular fibrillation must be indicated for arelatively prolonged period before the AED changes to a “no-shock”decision. As implemented in this AED example, stickiness mandatescertainty before a decision to shock is disregarded. In practice,stickiness also dictates that acute deteriorations in disease state aretreated aggressively while chronic, more slowly progressing diseasestates are treated in a more tempered fashion.

Thus, if the patient status indicates a status quo (block 330), nochanges in treatment or diagnosis are indicated and the routine returns.Otherwise, if the patient status indicates a change away from status quo(block 330), the relative quantum of change and the length of time overwhich the change has occurred is determinative. If the change ofapproximately 0.5 SD has occurred over the course of about one month(block 331), a gradually deteriorating condition exists (block 332) anda very tempered diagnostic, and if appropriate, treatment program isundertaken. If the change of approximately 1.0 SD has occurred over thecourse of about one week (clock 333), a more rapidly deterioratingcondition exists (block 334) and a slightly more aggressive diagnostic,and if appropriate, treatment program is undertaken. If the change ofapproximately 2.0 SD has occurred over the course of about one day(block 335), an urgently deteriorating condition exists (block 336) anda moderately aggressive diagnostic, and if appropriate, treatmentprogram is undertaken. If the change of approximately 3.0 SD hasoccurred over the course of about one hour (block 337), an emergencycondition exists (block 338) and an immediate diagnostic, and ifappropriate, treatment program is undertaken as is practical. Finally,if the change and duration fall outside the aforementioned ranges(blocks 331-338), an exceptional condition exists (block 339) and thechanges are reviewed manually, if necessary. The routine then returns.These threshold limits and time ranges may then be adapted dependingupon patient history and peer-group guidelines.

The present invention provides several benefits. One benefit is improvedpredictive accuracy from the outset of patient care when a referencebaseline is incorporated into the automated diagnosis. Another benefitis an expanded knowledge base created by expanding the methodologiesapplied to a single patient to include patient peer groups and theoverall patient population. Collaterally, the information maintained inthe database could also be utilized for the development of furtherpredictive techniques and for medical research purposes. Yet a furtherbenefit is the ability to hone and improve the predictive techniquesemployed through a continual reassessment of patient therapy outcomesand morbidity rates.

Other benefits include an automated, expert system approach to thecross-referral, consideration, and potential finding or elimination ofother diseases and health disorders with similar or related etiologicalindicators and for those other disorders that may have an impact nmyocardial ischemia. Although disease specific markers will prove veryuseful in discriminating the underlying cause of symptoms, manydiseases, other than myocardial ischemia, will alter some of the samephysiological measures indicative of myocardial ischemia. Consequently,an important aspect of considering the potential impact of otherdisorders will be, not only the monitoring of disease specific markers,but the sequencing of change and the temporal evaluation of more generalphysiological measures, for example heart rate, ECG ST-T wave changes,pulmonary artery diastolic pressure, and cardiac output, to reflectdisease onset, progression or regression in more than one type ofdisease process.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

1. A system for automated diagnosis of myocardial ischemia throughremote monitoring, comprising: a database module storing physiologicalmeasures comprising data either recorded on a regular basis by a medicaldevice or derived therefrom; a comparison module matching qualitativemeasures associated with the physiological measures; an analysis moduleremotely identifying indications of myocardial ischemia, by examiningthe qualitative measures for both of a reduction in exercise capacityand respiratory distress occurring contemporaneously, and evaluating thequalitative measures for angina that accompanies the reduction inexercise capacity and the respiratory distress; and a diagnostic moduledetermining a time course for each of the indications, and forming apatient status comprising an onset of myocardial ischemia conditioned onthe time course comprising a short duration.
 2. A system according toclaim 1, wherein the analysis module further compares each of thephysiological measures against an indicator threshold comprising one ofa finite cutoff value, weighted value, and statistical range, andfurther evaluates a potential onset, progression, regression, and statusquo of myocardial ischemia based on findings of the indicator thresholdcomparisons.
 3. A system according to claim 2, wherein the analysismodule further assigns the weighted value comprising a highest weight towall motion abnormalities in the reduction in exercise capacity andrespiratory distress examinations.
 4. A method for automated diagnosisof myocardial ischemia through remote monitoring, comprising: storingphysiological measures comprising data either recorded on a regularbasis by a medical device or derived therefrom; matching qualitativemeasures associated with the physiological measures; remotelyidentifying indications of myocardial ischemia, comprising: examiningthe qualitative measures for both of a reduction in exercise capacityand respiratory distress occurring contemporaneously; and evaluating thequalitative measures for angina that accompanies the reduction inexercise capacity and the respiratory distress; determining a timecourse for each of the indications; and forming a patient statuscomprising an onset of myocardial ischemia conditioned on the timecourse comprising a short duration.
 5. A method according to claim 4,further comprising: comparing each of the physiological measures againstan indicator threshold comprising one of a finite cutoff value, weightedvalue, and statistical range; and evaluating a potential onset,progression, regression, and status quo of myocardial ischemia based onfindings of the indicator threshold comparisons.
 6. A method accordingto claim 5, further comprising: assigning the weighted value comprisinga highest weight to wall motion abnormalities in the reduction inexercise capacity and respiratory distress examinations.
 7. A system forautomated diagnosis of myocardial ischemia through remote monitoring,comprising: a database module storing physiological measures comprisingdata either recorded on a regular bases by a medical device or derivedtherefrom; a comparison module matching qualitative measures associatedwith the physiological measures; an analysis module remotely identifyingindications of myocardial ischemia, by examining the qualitativemeasures for angina, and further evaluating the qualitative measures forone of a reduction in exercise capacity and of respiratory distress,which accompanies the angina; and a diagnostic module determining a timecourse for each of the indications, and forming a patient statuscomprising an onset of myocardial ischemia conditioned on the timecourse comprising a short duration.
 8. A system according to claim 7,wherein the analysis module further compares each of the physiologicalmeasure against an indicator threshold comprising one of a finite cutoffvalue, weighted value, and statistical range, and further evaluates apotential onset, progression, regression, and status quo of myocardialischemia based on findings of the indicator threshold comparisons.
 9. Asystem according to claim 8, wherein the analysis module further assignsthe weighted value comprising a highest weight to an ST segmentelevation in the angina examination.
 10. A method for automateddiagnosis of myocardial ischemia through remote monitoring, comprising:storing physiological measures comprising data either recorded on aregular basis by a medical device or derived therefrom; matchingqualitative measures associated with the physiological measures;remotely identifying indications of myocardial ischemia, comprising:examining the qualitative measures for angina; and further evaluatingthe qualitative measures for one of a reduction in exercise capacity andof respiratory distress, which accompanies the angina; determining atime course for each of the indications; and forming a patient statuscomprising an onset of myocardial ischemia conditioned on the timecourse comprising a short duration.
 11. A method according to claim 10,further comprising: comparing each of the physiological measures againstan indicator threshold comprising one of a finite cutoff value, weightedvalue, and statistical range; and evaluating a potential onset,progression, regression, and status quo of myocardial ischemia based onfindings of the indicator threshold comparisons.
 12. A method accordingto claim 11, further comprising: assigning the weighted value comprisinga highest weight to an ST segment elevation in the angina examination.13. A system for automated diagnosis of cardiac ischemia through STsegment monitoring, comprising: a database module storing physiologicalmeasures comprising data either recorded on a regular basis by animplantable medical device or derived therefrom; an analysis moduledetermining indications of cardiac ischemia, by identifying thephysiological measures that comprise electrocardial signals anddetecting a period of increased activity indicated thereby, anddetermining a deviation in electrical potential during an ST segment ofthe electrocardial signal within the period of increased activity; and adiagnostic module forming a patient status comprising a form of cardiacischemia based on the deviation.
 14. A system according to claim 13,wherein the analysis module further evaluates the deviation for one ofan elevated potential and a depressed potential during the ST segmentand further specifies the elevated potential as an indicator of thecardiac ischemia, wherein the form comprises myocardial ischemia.
 15. Asystem according to claim 14, wherein the analysis module furtherconfirms an absence of QRS duration of the electrocardial signal greaterthan 120 ms, and further declares the elevation of the ST segment as ana priori indicator of the myocardial ischemia when exceeding 2.0 mm. 16.A method for automated diagnosis of cardiac ischemia through ST segmentmonitoring, comprising: storing physiological measures comprising dataeither recorded on a regular basis by an implantable medical device orderived therefrom; determining indications of cardiac ischemia,comprising: identifying the physiological measures that compriseelectrocardial signals and detecting a period of increased activityindicated thereby; and determining a deviation in electrical potentialduring an ST segment of the electrocardial signal within the period ofincreased activity; and forming a patient status comprising a form ofcardiac ischemia based on the deviation.
 17. A method according to claim16, further comprising: evaluating the deviation for one of an elevatedpotential and a depressed potential during the ST segment; andspecifying the elevated potential as an indicator of the cardiacischemia, wherein the form comprises myocardial ischemia.
 18. A methodaccording to claim 17, further comprising: confirming an absence of QRSduration of the electrocardial signal greater than 120 ms; and declaringthe elevation of the ST segment as an a priori indicator of themyocardial ischemia when exceeding 2.0 mm.
 19. A system for automatedtreatment of myocardial ischemia through ST segment monitoring,comprising: a database module storing physiological measures comprisingdata either recorded on a regular basis by an implantable medical deviceor derived therefrom; an analysis module determining indications ofmyocardial ischemia, by identifying the physiological measures thatcomprise electrocardial signals and detecting a period of increasedactivity indicated thereby, determining an elevation in electricalpotential during an ST segment of the electrocardial signal within theperiod of increased activity, and delivering an intracardiac electricaltherapy to treat the indications of myocardial ischemia in response tothe elevation in electrical potential.
 20. A system according to claim19, wherein the analysis module further conforms an absence of QRSduration of the electrocardial signal greater than 120 ms, and furtherdeclares the elevation in electrical potential as an a priori indicatorof the myocardial ischemia when exceeding 2.0 mm.
 21. A system accordingto claim 19, wherein the analysis module further delivers a furthertherapy selected from the group comprising anticoagulation, antiplateletdrugs, beta-blockade, coronary vasodilators, afterload reduction, lipidlowering drugs, and mechanical therapies.
 22. A method for automatedtreatment of myocardial ischemia through ST segment monitoring,comprising: assembling physiological measures comprising data eitherrecorded on a regular basis by an implantable medical device or derivedtherefrom; determining indications of myocardial ischemia, comprising:identifying the physiological measures that comprise electrocardialsignals and detecting a period of increased activity indicated thereby;and determining an elevation in electrical potential during an STsegment of the electrocardial signal within the period of increasedactivity; and delivering an intracardiac electrical therapy to treat theindications of myocardial ischemia in response to the elevation inelectrical potential.
 23. A method according to claim 22, furthercomprising: confirming an absence of QRS duration of the electrocardialsignal greater than 120 ms; and declaring the elevation in electricalpotential as an a priori indicator of the myocardial ischemia whenexceeding 2.0 mm.
 24. A method according to claim 22, furthercomprising: delivering a further therapy selected from the groupcomprising anticoagulation, antiplatelet drugs, beta-blockade, coronaryvasodilators, afterload reduction, lipid lowering drugs, and mechanicaltherapies.